Environmental drivers of vector-borne and zoonotic diseases

Leveraging remote sensing for Public Health

Author

Verónica Andreo

About me


  • Researcher and lecturer at Instituto Gulich
  • Background: Dr. in Biology, MSc. in Spatial Information Applications
  • Remote sensing and geospatial applications in disease ecology
  • Member of the GRASS GIS Dev Team & project chair; OSGeo Charter member & FOSS4G enthusiast

https://veroandreo.gitlab.io/

Overview

  • Motivation
  • Health Geography
  • Disease Ecology
  • Leveraging remote sensing for Disease Ecology
    • Resolution vs scale
    • How can we use RS?
    • Examples
  • Gaps, challenges and opportunities
  • Conclusion

https://sdgs.un.org/goals/goal3

Neglected Tropical Diseases (NTD)


You all have seen this, right?



Health Geography


Environmental health: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.

Disease ecology: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.

Health care delivery and access: spatial patterns of health care provision and patient behavior.

Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Mencionar potenciales usos y aplicaciones del SR en los 3 campos

Health Geography


Environmental health: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.

Disease ecology: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.

Health care delivery and access: spatial patterns of health care provision and patient behavior.

While RS has applications in all fields, I’ll focus on those related to disease ecology as it is where I have worked the most

Disease Ecology I



The main objective is to understand the influence of environmental factors and to predict when and where a disease is most likely to occur

decision making, planning of prevention, management or response actions, etc.

Disease Ecology II

Lambin et al. (2010)

  1. Landscape attributes may influence the level of transmission of an infection
  2. Spatial variations in disease risk depend not only on the presence and area of critical habitats but also on their spatial configuration
  3. Disease risk depends on the connectivity of habitats for vectors and hosts
  4. The landscape is a proxy for specific associations of reservoir hosts and vectors linked with the emergence of multi-host diseases
  5. To understand ecological factors influencing spatial variations of disease risk, one needs to take into account the pathways of pathogen transmission between vectors, hosts, and the physical environment
  6. The emergence and distribution of infection through time and space is controlled by different factors acting at multiple scales
  7. Landscape and meteorological factors control not just the emergence but also the spatial concentration and spatial diffusion of infection risk
  8. Spatial variation in disease risk depends not only on land cover but also on land use, via the probability of contact between, on one hand, human hosts and, on the other hand, infectious vectors, animal hosts or their infected habitats
  9. The relationship between land use and the probability of contact between vectors and animal hosts and human hosts is influenced by land ownership
  10. Human behaviour is a crucial controlling factor of vector-human contacts, and of infection

Use of RS in Health applications

Viana et al. (2017)

Most common RS variables used


  • LST
  • Precipitation
  • NDVI
  • LULC
  • Elevation
  • NDWI

Parselia et al. (2019)

Remote sensing basic features

However, we should take into account some basic features of remote sensing before selecting which data to use

Remote sensing & scale I


Pearson and Dawson (2003)

Remote sensing & scale II

Leitão and Santos (2019), Lechner et al. (2020), Rumiano et al. (2020)

How to apply RS in disease ecology?


General approach used in (disease) ecology

  • To map the response variables, i.e., species occurrence or abundance, infections, disease cases
  • To map the predictor variables
  • To validate predictions

Let’s have a look at some real cases…

Detecting and mapping species occurrences


  • Very high resolution (VHR) imagery
  • Hyperspectral data (esp. for plant species)
  • Direct and indirect counting

(a) Emperor penguins. (b) Elephants

Great gerbil burrows classification

Wang et al. (2019)

Detecting and mapping species occurrences


Pine beetle infection

Meng et al. (2022)

Time series analysis of satellite products

  • MODIS LST temporal and spatial reconstruction
  • Estimation of relevant indices (GRASS GIS temporal framework!)
  • Detection of spatial and temporal clusters of favorable conditions for the occurrence of West Nile Fever cases in Greece

LST gap-filling process


Co-cluster of derived LST indices

Metz et al. (2017), Andreo et al. (2018)

Environmental risk of Dengue

  • MODIS LST is used to estimate number of extrinsic incubation periods (EIP) that virus might complete; the higher this number, the higher the environmental risk

CONAE Geoportal

Porcasi et al. (2012), https://github.com/InstitutoGulich/RiesgoAmbiental

SDM & GIS based approach for HPS risk map


We combined a rescaled probability map of the host with one of the human cases to determine levels of transmission risk


Andreo et al. (2011), Andreo et al. (2014)

Cutaneous leishmaniasis and LULCC


Change map

CL Prediction map

Andreo et al. (2022)

Mosquitoes: towards operational high res maps

Workflow

Andreo et al. (2021a)

Spatial distribution of temporal patterns

  • Temporal and spatial patterns in Aedes aegypty in Córdoba
  • Association with variables derived from Sentinel 2 imagery analysis to predict temporal patterns over the whole city.

Andreo et al. (2021b)

Urban environmental characterisation for the distribution of ovitraps

  • Object-based classification of VHR imagery
  • Landscape metrics for polygons
  • Clustering to find groups of similar polygons
  • Stratified distribution of ovitraps

MSc thesis, Carla Rodriguez.

Grippa et al. (2017), Georganos et al. (2018), Rodriguez Gonzalez et al.

Predictive system based on population dynamics and weather forecasting


Development of an early warning system (EWS) for dengue. PhD candidate, Tomás San Miguel.

Aguirre et al. (2021)

Online surveillance system



Online surveillance system

Other projects under development

Incidence of asthma as a function of remotely sensed air quality and LULCC. PhD candidate, Abraham Coiman.

Distribution of congenital diseases and access to health. PhD candidate, Carla Rodriguez Gonzalez.

Epidemiological characterisation of intestinal parasite infection in children. PhD candidate, Matias Scavuzzo.

Geospatial modelling of malnutrition in children and adolescents. PhD candidate, Micaela Campero.

Environmental variables associated with non-communicable diseases. Dr. Juan Diego Pinotti and Dr. Ximena Porcasi.

Challenges and gaps - RS

  • Trade-off between different RS resolutions, the problem under study, the data and methods available
  • Gaps in optical RS: clouds, shadows in optical RS (spatial and temporal interpolations)
  • Need for corrections if high level data is not suitable (ARD)
  • Limited access to VHR, LiDAR, Hyper-spectral (US$, not easy to scale yet)
  • Investment and capacity building: huge volumes of data vs. limited bandwidth, storage and computational capacity (cloud computing, parallelisation | learning time and US$)

Field data will always be needed! :)

Challenges and gaps - Ecology and Health


  • Missing baseline distribution information of hosts, vectors, infection
  • Updating and digitisation of disease cases and intervention data, data still missing in large parts of the world
  • Harmonisation of records at different levels, i.e., municipal, provincial, national
  • Facilitating access to (aggregated) health data
  • Political decision and resource allocation

Opportunities: low hanging fruits?

  • SAR data to avoid clouds, e.g., SAOCOM to estimate soil moisture
  • Open LiDAR data, e.g., GEDI onboard of ISS
  • GEE vs open source solutions openEO.cloud, actinia, OpenPlains? ;-)


New missions: hyper-spectral for all

  • A number of recent and upcoming missions for hyper-spectral data: PRISMA (recently made open), EnMap, CHIME, TIRS

http://database.eohandbook.com/database/instrumenttable.aspx,

Specialized cameras onboard drones

  • Cheaper UAVs with different types of cameras, e.g. thermal multi- or hyper-spectral sensors to detect and count animals in inaccessible places

Carrasco-Escobar et al. (2022)

Thanks!



veronica.andreo@ig.edu.ar

VeronicaAndreo

Unhealthy lab

References

Aguirre, E., Andreo, V., Porcasi, X., Lopez, L., Guzman, C., González, P., and Scavuzzo, C. M. (2021), “Implementation of a proactive system to monitor Aedes aegypti populations using open access historical and forecasted meteorological data,” Ecological Informatics, 64, 101351. https://doi.org/10.1016/j.ecoinf.2021.101351.
Andreo, V., Cuervo, P. F., Porcasi, X., Lopez, L., Guzman, C., and Scavuzzo, C. M. (2021a), “Towards a workflow for operational mapping of Aedes aegypti at urban scale based on remote sensing,” Remote Sensing Applications: Society and Environment, 23, 100554. https://doi.org/10.1016/j.rsase.2021.100554.
Andreo, V., Glass, G., Shields, T., Provensal, C., and Polop, J. (2011), “Modeling Potential Distribution of Oligoryzomys longicaudatus, the Andes Virus (Genus: Hantavirus) Reservoir, in Argentina,” EcoHealth, 8, 332–348.
Andreo, V., Izquierdo-Verdiguier, E., Zurita-Milla, R., Rosa, R., Rizzoli, A., and Papa, A. (2018), “Identifying Favorable Spatio-Temporal Conditions for West Nile Virus Outbreaks by Co-Clustering of Modis LST Indices Time Series,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia: IEEE, pp. 4670–4673. https://doi.org/10.1109/IGARSS.2018.8519542.
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Andreo, V., Porcasi, X., Guzman, C., Lopez, L., and Scavuzzo, C. M. (2021b), “Spatial Distribution of Aedes aegypti Oviposition Temporal Patterns and Their Relationship with Environment and Dengue Incidence,” Insects, 12, 919. https://doi.org/10.3390/insects12100919.
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Lechner, A. M., Foody, G. M., and Boyd, D. S. (2020), “Applications in Remote Sensing to Forest Ecology and Management,” One Earth, 2, 405–412. https://doi.org/10.1016/j.oneear.2020.05.001.
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Meng, R., Gao, R., Zhao, F., Huang, C., Sun, R., Lv, Z., and Huang, Z. (2022), “Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest,” Remote Sensing of Environment, 269, 112847. https://doi.org/10.1016/j.rse.2021.112847.
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Parselia, E., Kontoes, C., Tsouni, A., Hadjichristodoulou, C., Kioutsioukis, I., Magiorkinis, G., and Stilianakis, N. I. (2019), “Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review,” Remote Sensing, 11, 1862. https://doi.org/10.3390/rs11161862.
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Porcasi, X., Rotela, C. H., Introini, M. V., Frutos, N., Lanfri, S., Peralta, G., De Elia, E. A., Lanfri, M. A., and Scavuzzo, C. M. (2012), “An operative dengue risk stratification system in Argentina based on geospatial technology,” Geospatial Health, 6, S31–S42. https://doi.org/10.4081/gh.2012.120.
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Extra slides

App to count mosquito eggs in ovitraps pics


https://ovitrap-monitor.netlify.app/